Fix documentation on OptunaSearchCV

docs/getting_started.rst

@@ -96,5 +96,5 @@ The following optional dependencies are available:
   module is not compatible with newer Python versions and it is unmaintained.
 * ``skopt``: Using the ``"bayes"`` searcher (:class:`~skopt.BayesSearchCV`)
   requires the `scikit-optimize`_ package.
-* ``optuna``: Using the ``"optuna"`` searcher (:class:`~optuna_integration.sklearn.OptunaSearchCV`) requires the `Optuna`_ and `optuna_integration`_ packages.
+* ``optuna``: Using the ``"optuna"`` searcher (:class:`~optuna_integration.OptunaSearchCV`) requires the `Optuna`_ and `optuna_integration`_ packages.
 * ``all``: Install all optional functional dependencies (except ``deslib``).

examples/99_docs/run_hyperparameters_docs.py

@@ -255,7 +255,7 @@
 # Other searchers that ``julearn`` provides are the
 # :class:`~sklearn.model_selection.RandomizedSearchCV`,
 # :class:`~skopt.BayesSearchCV` and
-# :class:`~optuna_integration.sklearn.OptunaSearchCV`.
+# :class:`~optuna_integration.OptunaSearchCV`.
 #
 # The randomized searcher
 # (:class:`~sklearn.model_selection.RandomizedSearchCV`) is similar to the
@@ -275,7 +275,7 @@
 # :class:`~skopt.BayesSearchCV` documentation, including how to specify
 # the prior distributions of the hyperparameters.
 #
-# The Optuna searcher (:class:`~optuna_integration.sklearn.OptunaSearchCV`)
+# The Optuna searcher (:class:`~optuna_integration.OptunaSearchCV`)
 # uses the Optuna library to find the best hyperparameter set. Optuna is a
 # hyperparameter optimization framework that has several algorithms to find
 # the best hyperparameter set. For more information, see the

julearn/api.py

@@ -142,7 +142,7 @@ def run_cross_validation(  # noqa: C901
             :class:`~sklearn.model_selection.RandomizedSearchCV`
           * ``"bayes"`` : :class:`~skopt.BayesSearchCV`
           * ``"optuna"`` :
-            :class:`~optuna_integration.sklearn.OptunaSearchCV`
+            :class:`~optuna_integration.OptunaSearchCV`
           * user-registered searcher name : see
             :func:`~julearn.model_selection.register_searcher`
           * ``scikit-learn``-compatible searcher

julearn/pipeline/pipeline_creator.py

@@ -944,7 +944,7 @@ def _prepare_hyperparameter_tuning(
             :class:`~sklearn.model_selection.RandomizedSearchCV`
           * ``"bayes"`` : :class:`~skopt.BayesSearchCV`
           * ``"optuna"`` :
-            :class:`~optuna_integration.sklearn.OptunaSearchCV`
+            :class:`~optuna_integration.OptunaSearchCV`
           * user-registered searcher name : see
             :func:`~julearn.model_selection.register_searcher`
           * ``scikit-learn``-compatible searcher

pyproject.toml

@@ -61,7 +61,7 @@ docs = [
     "towncrier<24",
     "scikit-optimize>=0.10.0,<0.11",
     "optuna>=3.6.0,<3.7",
-    "optuna_integration>=3.6.0,<3.7",
+    "optuna_integration>=3.6.0,<4.1",
 ]
 deslib = ["deslib>=0.3.5,<0.4"]
 viz = [
@@ -72,7 +72,7 @@ viz = [
 skopt = ["scikit-optimize>=0.10.0,<0.11"]
 optuna = [
     "optuna>=3.6.0,<3.7",
-    "optuna_integration>=3.6.0,<3.7",
+    "optuna_integration>=3.6.0,<4.1",
 ]
 # Add all optional functional dependencies (skip deslib until its fixed)
 # This does not include dev/docs building dependencies

tox.ini

@@ -16,7 +16,7 @@ deps =
     seaborn
     scikit-optimize>=0.10.0,<0.11
     optuna>=3.6.0,<3.7
-    optuna_integration>=3.6.0,<3.7
+    optuna_integration>=3.6.0,<4.1
 commands =
     pytest {toxinidir}/julearn
 
@@ -45,7 +45,7 @@ deps =
     param
     scikit-optimize>=0.10.0,<0.11
     optuna>=3.6.0,<3.7
-    optuna_integration>=3.6.0,<3.7
+    optuna_integration>=3.6.0,<4.1
 commands =
     pytest -vv {toxinidir}/julearn
 
@@ -69,7 +69,7 @@ deps =
     param
     scikit-optimize>=0.10.0,<0.11
     optuna>=3.6.0,<3.7
-    optuna_integration>=3.6.0,<3.7
+    optuna_integration>=3.6.0,<4.1
 commands =
     pytest --cov={envsitepackagesdir}/julearn --cov=./julearn --cov-report=xml --cov-report=term -vv